Impaired cerebral microvascular endothelial cells integrity due to elevated dopamine in myasthenic model

Myasthenia gravis is an autoimmune disease characterized by pathogenic antibodies that target structures of the neuromuscular junction. However, some patients also experience autonomic dysfunction, anxiety, depression, and other neurological symptoms, suggesting the complex nature of the neurological manifestations. With the aim of explaining the symptoms related to the central nervous system, we utilized a rat model to investigate the impact of dopamine signaling in the central nervous and peripheral circulation. We adopted several screening methods, including western blot, quantitative PCR, mass spectrum technique, immunohistochemistry, immunofluorescence staining, and flow cytometry. In this study, we observed increased and activated dopamine signaling in both the central nervous system and peripheral circulation of myasthenia gravis rats. Furthermore, changes in the expression of two key molecules, Claudin5 and CD31, in endothelial cells of the blood–brain barrier were also examined in these rats. We also confirmed that dopamine incubation reduced the expression of ZO1, Claudin5, and CD31 in endothelial cells by inhibiting the Wnt/β-catenin signaling pathway. Overall, this study provides novel evidence suggesting that pathologically elevated dopamine in both the central nervous and peripheral circulation of myasthenia gravis rats impair brain–blood barrier integrity by inhibiting junction protein expression in brain microvascular endothelial cells through the Wnt/β-catenin pathway. Graphical Abstract Supplementary Information The online version contains supplementary material available at 10.1186/s12974-023-03005-3.


Supplementary Note 1: Sample Preparation Protein extraction
The sample was ground into cell powder using liquid nitrogen and then transferred to a 5-mL centrifuge tube.Then, four volumes of lysis buffer (8 M urea, 1% protease inhibitor cocktail) were added to this powder.The mixture underwent sonication three times on ice, employing a highintensity ultrasonic processor (Scientz).Subsequently, the debris was eliminated through centrifugation at 12,000 g at 4 °C for 10 minutes.The resulting supernatant was collected, and the protein concentration was assessed using a BCA kit following the manufacturer's instructions.

Digestion
To initiate digestion, each sample containing 300 μg of protein was slowly added to reach a final concentration of 20% (m/v) TCA for protein precipitation.The mixture was vortexed for thorough mixing and then incubated at 4 °C for 2 hours.The resulting precipitate was collected through centrifugation at 4,500 g for 5 minutes at 4 °C.The precipitated protein underwent three washes with 1 mL of pre-cooled acetone.Subsequently, the precipitate was dispersed using 300 μL of 200 mM TEAB through ultrasonication.After that, trypsin was added at a 1:50 trypsin-to-protein mass ratio and left overnight.The sample was reduced with 5 mM dithiothreitol for 60 minutes at 37 °C and then alkylated with 11 mM iodoacetamide for 45 minutes at room temperature in darkness.Finally, the peptides were desalted using a Strata X SPE column.

TMT labeling
Initially, tryptic peptides were dissolved in 0.5 M TEAB solution.Subsequently, each channel of peptides was labeled with the respective TMT reagent according to ThermoFisher Scientific's protocol and incubated at room temperature for 2 hours.Five microliters from each sample were pooled and subjected to desalting, followed by analysis using mass spectrometry to assess labeling efficiency.Once labeling efficiency was confirmed, samples were quenched by adding 5% hydroxylamine.The pooled samples were then desalted using a Strata X C18 SPE column (Phenomenex) and dried via vacuum centrifugation.

HPLC fractionation
The sample was fractionated using high pH reverse-phase HPLC with an Agilent 300Extend C18 column (5 μm particles, 4.6 mm ID, 250 mm length).The wavelength was set to 214 nm, and the column oven temperature was maintained at 35°C with 95% buffer A (2% ACN, pH 9.0 adjusted by ammonia) and 5% buffer B (98% ACN, pH 9.0 adjusted by ammonia) for at least 30 minutes.Once a flat baseline was obtained, 1 mL of buffer A was added to the peptide sample, which was then vortexed for dissolution.After centrifugation at 12,000 g for 5 minutes, the supernatant was transferred to a new tube.The sample was centrifuged again, the supernatant collected, and loaded onto the HPLC.The separation process was initiated, and simultaneously, the automatic collector was activated.The samples were collected at 1 min per tube from the 9 th to the 72 nd tube, resulting in a total of 64 tubes.Finally, the peptides from these tubes were combined into 6 fractions and dried using vacuum centrifugation.

Analysis
After separation, the peptides were analyzed using an Orbitrap Exploris 480 mass spectrometer (ThermoFisher Scientific) equipped with a nano-electrospray ion source.An electrospray voltage of 2,300 V was applied, and FAIMS compensation voltage (CV) was set at -45 V.Both precursors and fragments were examined at the Orbitrap detector.Full MS scans were conducted at a resolution of 60,000, covering the mass-to-charge ratio (m/z) range of 400 and 1,200.The MS/MS scans were initiated using TurboTMT with a fixed first mass of 110 m/z and a resolution of 15,000.Subsequently, up to 25 most abundant precursors were chosen for further MS/MS analyses, with a dynamic exclusion duration of 30 seconds.HCD fragmentation was executed at a normalized collision energy (NCE) of 35%.The automatic gain control (AGC) target was set to 100%, with an intensity threshold of 10000 ions/s and an Auto maximum injection time.

Database searching
The obtained MS/MS data were analyzed using Proteome Discoverer software (version 2.4.1.15).Tandem mass spectra were searched in the Rattus norvegicus database (containing 29,934 entries) combined with a reverse decoy database.Trypsin/P was chosen as the cleavage enzyme, allowing up to 2 missing cleavages.The mass tolerance for precursor ions was set at 20 ppm in the initial search and 5 ppm in the main search, while the mass tolerance for fragment ions was set at 0.02 Da.Carbamidomethyl on cysteine was considered a fixed modification, and acetylation on protein N-terminal and oxidation on methionine were treated as variable modifications.To ensure data falsifiability, the false discovery rate (FDR) was controlled at less than 1%.
The Gene Ontology (GO) database is a major bioinformatics initiative that aims to develop a computational representation of our evolving knowledge of how genes encode biological functions at the molecular, cellular, and tissue system levels.It is the world's largest source of information on the functions of genes.This knowledge is both human-readable and machine-readable, and serves as a foundation for computational analysis of large-scale molecular biology and genetics experiments in biomedical research.Gene Ontology describes knowledge of the biological domain with respect to three aspects: molecular function (MF), biological process (BP), and cellular component.
Molecular function refers to the molecular-level activities performed by gene products.Molecular function terms describe activities that occur at the molecular level, such as catalysis or transport.These functions generally correspond to activities that can be performed by individual gene products, but some activities are performed by molecular complexes composed of multiple gene products.Examples of broad functional terms are catalytic activity and transporter activity; examples of narrower functional terms are adenylate cyclase activity or Toll-like receptor binding.To avoid confusion between gene product names and their molecular functions, GO molecular functions are often appended with the word activity (a protein kinase would have the GO molecular function protein kinase activity).
Biological process represents the larger processes, or "biological programs" accomplished by multiple molecular activities.Examples of broad biological process terms are DNA repair or signal transduction.More specific terms might include pyrimidine nucleobase biosynthetic process or glucose transmembrane transport.At present, the GO does not try to represent the dynamics or dependencies that would be required to fully describe a pathway.
Cellular component refers to the locations relative to cellular structures in which a gene product performs a function.These can be either cellular compartments (e.g., mitochondrion) or stable macromolecular complexes of which they are parts (e.g., the ribosome).Unlike the other aspects of GO, cellular component classes refer not to processes but rather a cellular anatomy.
GO terms are assigned to gene products through annotation.GO annotations for gene products can change over time as the evolvement of the understanding of gene functions.Annotation involves associating each gene product with the appropriate GO terms that describe its molecular function, biological process, and cellular component.The annotation process is carried out by members of the GO Consortium (GOC), which is a community-based bioinformatics project.The GOC uses structured controlled vocabularies to classify gene product function.

KEGG PATHWAY
The Kyoto Encyclopedia of Genes and Genomes (KEGG) is a database resource that aims to understand high-level functions and utilities of the biological system, such as the cell, the organism, and the ecosystem, from molecular-level information.One of the most important and commonly used sub-databases in KEGG is the KEGG PATHWAY.It is a collection of manually drawn pathway maps representing our knowledge of the molecular interaction, reaction, and relation networks.These pathway maps are created by researchers based on existing research literature.KEGG PATHWAY database covers seven major categories, which are, metabolism, genetic information processing, environmental information processing, cellular process, organismal systems, human diseases, and drug development.These pathway maps are created based on existing research literature, making them a reliable source of information.KEGG PATHWAY database is widely used by bioinformaticians.It provides a comprehensive knowledge base for understanding high-level functions and utilities of the biological system, from molecular-level information to organismal systems.This makes it a valuable resource for interpreting genome sequences and other high-throughput data.

Gene set enrichment analysis (GSEA)
Gene Set Enrichment Analysis (GSEA) is a computational approach designed to determine whether a predefined set of genes exhibits statistically significant, consistent differences between two biological states (e.g.phenotypes).It is a major bioinformatics initiative to develop a computational representation of our evolving knowledge of how genes encode biological functions at the molecular, cellular and tissue system levels.
GSEA utilizes statistical techniques to identify gene groups that are significantly enriched or depleted.Researchers performing high-throughput experiments that yield sets of genes often want to retrieve a functional profile of that gene set, in order to better understand the underlying biological processes.
GSEA method employs a weighted procedure where increments to the running sum are proportionate to the rank metric for the gene set.The process begins by ranking all genes in the dataset based on their correlation with a specified phenotype.Various metrics measuring the association between gene expression and the phenotype can be utilized for this purpose, resulting in a ranked gene list.Subsequently, the enrichment score (ES) is computed by traversing the ranked gene list.If a gene belongs to the gene set of interest, the running-sum statistic increases; conversely, if it does not, the statistic decreases.The magnitude of this increment is contingent upon the correlation of the gene with the phenotype.The ES represents the maximum deviation from zero encountered during this traversal and corresponds to a weighted Kolmogorov-Smirnovlike statistic.
A permutation test is performed to estimate the significance level of the ES.This entails shuffling the phenotype labels, preserving gene-gene correlations.The ES is recalculated for each permutation, creating a null distribution.The nominal p-value of the observed ES is then calculated relative to this null distribution, providing an estimation of its significance.Given the multitude of gene sets tested for enrichment in GSEA, it becomes imperative to correct for multiple hypothesis testing to prevent false positives.This correction is achieved by calculating a False Discovery Rate (FDR) using Benjamini-Hochberg correction corresponding to each ES.

Protein-protein interaction and STRING database
Proteins rarely act alone as their functions tend to be regulated.Many molecular processes within a cell are carried out by molecular machines that are built from numerous protein components organized by their protein-protein interactions (PPIs).These physiological interactions make up the so-called interactomes of the organism, while aberrant PPIs are the basis of multiple aggregationrelated diseases, such as Creutzfeldt-Jakob and Alzheimer's diseases.
PPIs are physical contacts of high specificity established between two or more protein molecules as a result of biochemical events steered by interactions that include electrostatic forces, hydrogen bonding, and the hydrophobic effect.
PPIs have been studied with many methods and from different perspectives, such as biochemistry, quantum chemistry, molecular dynamics, signal transduction, among others.All this information enables the creation of large protein interaction networks that empower the current knowledge on biochemical cascades and molecular etiology of disease, as well as the discovery of putative protein targets of therapeutic interest.PPI network is an important ingredient for the system-level understanding of cellular processes.Such networks can be used for filtering and assessing functional genomics data and for providing an intuitive platform for annotating structural, functional, and evolutionary properties of proteins.Exploring predicted interaction networks can suggest new directions for future experimental research and provide cross-species predictions for efficient interaction mapping.Therefore, one of the widely used databases is STRING.
The interactions in STRING include both direct (physical) and indirect (functional) associations.They stem from computational prediction, from knowledge transfer between organisms, and from interactions aggregated from other (primary) databases.It contains information from numerous sources, including experimental data, computational prediction methods, and public text collections.It is freely accessible, and it is regularly updated.The resource also serves to highlight functional enrichments in user-provided lists of proteins, using several functional classification systems such as GO, Pfam, and KEGG.Therefore, various computational predictions can be inspected from different designated views.

t-distributed Stochastic Neighbor Embedding (t-SNE)
t-distributed Stochastic Neighbor Embedding (t-SNE) is a statistical method for visualizing highdimensional data by giving each datapoint a location in a two or three-dimensional map.It is often used to visualize complex datasets into two and three dimensions, allowing us to understand more about underlying patterns and relationships in the data.
The t-SNE algorithm involves two main stages.First, t-SNE constructs a probability distribution over pairs of high-dimensional objects in such a way that similar objects are assigned a higher probability while dissimilar points are assigned a lower probability.Then, t-SNE defines a similar probability distribution over the points in the low-dimensional map, and it minimizes the Kullback-Leibler divergence (KL divergence) between the two distributions with respect to the locations of the points in the map.
Both t-SNE and principal component analysis (PCA) are dimensional reduction techniques that have different mechanisms and work best with different types of data.PCA is a linear technique that works best with data that has a linear structure.It seeks to identify the underlying principal components in the data by projecting onto lower dimensions, minimizing variance, and preserving large pairwise distances.But t-SNE is a nonlinear technique that focuses on preserving the pairwise similarities between data points in a lower-dimensional space.t-SNE is concerned with preserving small pairwise distances, whereas PCA focuses on maintaining large pairwise distances to maximize variance.

Supplementary Figure 1
Cerebral microvascular extraction and identification in CFA and EAMG rats.The cerebral microvascular was confirmed by the tubular structure via (A) optical microscope and (B) HE staining.(C) the absence of the neuronal markers (Syp and Tubb3 genes) via qPCR.Statistical analysis was performed using t test, *** P < 0.001, **** P < 0.0001.Supplementary Figure 3 The expression of DRD1-DRD5 and Scl6a3 genes in T cells (A) and B cells (B) of CFA and EAMG groups.Statistical analysis was conducted using t test, * P < 0.05, ** P < 0.01, ns = no significance.

5
The impact of SCH23390 (dopamine D1 receptor antagonist) and Haloperidol (dopamine D2 receptor antagonist) on bEnd.3 cells was investigated.(A-B) The proliferative effects of SCH23390 and Haloperidol on bEnd.3 cell line were assessed using CCK-8 assays.(C-D) The expression of CD31 was significantly increased upon incubation with both SCH23390 and Haloperidol, while Claudin5 remained unchanged.Additionally, the expression levels of Wnt3a in bEnd.3 cell line were examined through western blot, showing no significant changes in SCH23390 and Haloperidoltreated cell lines.However, the results examined through western blot also showed elevated levels of p-GSK3β and active-β-catenin in SCH23390-treated cell line.Statistical analysis was conducted using one-way ANOVA, * P < 0.05, ** P < 0.01, *** P < 0.001, **** P < 0.0001, ns = no significant difference.The effects of dopamine on T and B cells were examined.(A) Upon dopamine incubation, the mean fluorescence intensity (MFI) level of Th17 cells were significantly increased, whereas the MFI of Th1 and Treg cells exhibited no significant changes.(B) The dopamine exposure resulted in a significant increase in the MFI of CD86 + B cells and CD69 + B cells.Conversely, the MFI of CD80 + B cells decreased, and MHCII + B cells remained unsignificant.Statistical analysis was conducted using t test, * P < 0.05, ** P < 0.01, ns = no significant difference.